blackest Loc: Ireland

There are a few reduction methods nearest neighbor bilinear bicubic, bicubic sharpen, Lanczos, sinc. The link i gave before talks about some of these.
Its math and quite complicated math at that e.g
https://en.wikipedia.org/wiki/Lanczos_resampling (Lanczos was a Hungarian)

To be fair the math is beyond me, its easier just to say bicubic (sharper) is probably the best downsizing option in photoshop, there is a bicubic smoother for upsizing. If you go back to that link there is evaluation of other methods, Lightroom's resizing may be better than whats available in Photoshop, the testing suggested the sharpening in bicubic sharper was too aggressive and sharpened noise as well as detail. It's worth looking through that site to see the evaluation, he does seem to have better methods for resizing but they use a command line tool and i haven't found the scripts for it yet.


These other links may be of interest resizing does seem to be a legitimate way to improve image quality and some methods are better than others.
I've found a few links that may be of interest.

https://svi.nl/PixelBinning
https://www.androidauthority.com/huawei-p20-pro-vs-lumia-1020-857050/
https://www.adimec.com/reducing-noise-and-increasing-camera-frame-rate-through-binning-on-sensor-binning-versus-digital-binning/

R.G. Loc: Scotland

Thanks for the links.

carl hervol Loc: jacksonville florida

dido dido

Gene51 Loc: Yonkers, NY, now in LSD (LowerSlowerDelaware)

Take a look at this link:

https://photographylife.com/why-downsampling-an-image-reduces-noise

carl hervol Loc: jacksonville florida

You will not see a difference between iso 100 and iso 400 you are thinking to much you are making a job out of fun I personally have better thing to think about my health my family and what I'm going to have for supper.

blackest Loc: Ireland

I read that page last week
After a long evening’s thought on the subject, and running a few questions past my friend and fellow engineer, I believe I have a (reasonable, though perhaps not perfect!) handle on the subject…

If the image signal and the image noise had similar properties, averaging neighboring pixels in order to reduce the resolution would not improve the signal-to-noise ratio. However, signal and noise have different properties.

There is (in general) no relationship between the noise in neighboring pixels. Technical junkies call this “no correlation”.

Correlation is the long-term average of the product of two signals N1 x N2. If two signals have no correlation, then the mean of their product is zero.

The signal in neighboring pixels has a high degree of correlation. If you add uncorrelated signals, then their “power” is added, meaning the combined signal is the square root of the combined power.

N_comb = sqrt(N1^2+N2^2) and for N1 = N2 = N we get N_comb = sqrt(2)*N, where N1, N2 are root-mean-square (RMS) values of the noise.

However, if signals are highly correlated, then their sum is effectively the sum of their magnitudes:

S_comb = S1+S2 and for S1=S2=S we get S_comb = 2*S

So, if we add the content of two neighboring pixels, we get:

SNR_comb = S_comb/N_comb = sqrt(2)*(S/N)

So, the signal-to-noise increases by square root of two, which is about 40%.

Now, you may say that the signal in neighboring pixels is not always 100% correlated. The correlation between the signals depends on the image content. If the image content is very smooth, the correlation is high. If the image content varies very fast, the correlation is low. Of course, noise will be more noticeable in smooth areas and the effect of resampling the image will be stronger.



So that seems to say you can get up to a 40% improvement by resizing especially where detail is low anyway its less effective in highly detailed areas but that tends to be where noise is hardest to spot

selmslie Loc: Fernandina Beach, FL, USA

That's a mathematically intense explanation that probably went over a lot of heads.

You could also use a sensor with larger pixels in the first place.

A Df with only 16MP is inherently less susceptible to noise than a 24, 36 or 45 MP sensor if for no other reason that you are less likely to look at a larger version at 100%.

You might also discover that you don't really need all of those extra MP for most of your images.